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Age-appropriate BMI cut-points for cardiometabolic health risk: a cross-sectional analysis of the Canadian Longitudinal Study on Aging

International Journal of Obesity volume 46, pages 1027–1035 (2022)Cite this article

Subjects

Abstract

Objectives

Body composition changes that occur with aging pose unique health risks to older adults. The current World Health Organization (WHO) body mass index (BMI) cut-points may not accurately reflect health risks in older adults (65+). Prior findings suggest those classified as overweight may be conferred survival advantages. This study aims to define age-specific BMI cut-points for adults (45–64, 65–74, and 75–85 years) associated with cardiometabolic outcomes, and compare the performance of these thresholds to the WHO BMI thresholds using cardiometabolic conditions and frailty as outcomes.

Methods

Using baseline data from the comprehensive cohort of the Canadian Longitudinal Study on Aging (N = 30,097), a classification and regression tree cross-sectional analysis was conducted to derive age-specific BMI cut-points based on cardiometabolic health risk. The area under the receiver operating curve (AUC), sensitivity, and specificity were estimated. Agreement with waist circumference was conducted.

Results

For older adults (65–74 and 75+ years old), the BMI threshold for identifying overweight increased to 26.9 and 26.6, respectively, from the WHO definition of 25.0 kg/m2. For obesity, the thresholds were revised to 29.0 and 30.9, respectively, from 30.0. The largest improvements to AUC occurred in older adults (65+). Across all age-sex stratifications, the new overweight threshold demonstrated lower sensitivity and higher specificity compared to the traditional threshold. Age-specific BMI thresholds demonstrated higher agreement with waist circumference for some age-sex stratifications and poor performance with hearing.

Conclusions

Age-appropriate BMI thresholds for older adults may improve classification by health risk compared to standard WHO cut-points. A higher overweight threshold but lower obesity cut-points may be best suited to this demographic.

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Acknowledgements

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset Baseline Comprehensive dataset version 3.0, under Application Number 160313. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. PR holds a Tier 1 Canada Research Chair in Geroscience and the Raymond and Margaret Labarge Chair in Research and Knowledge Application for Optimal Aging. Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. Lauren Griffith is supported by the McLaughlin Foundation Professorship in Population and Public Health.

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Authors and Affiliations

  1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada

    Ayesha A. Javed, Jinhui Ma, Laura N. Anderson, Alexandra J. Mayhew, Lauren E. Griffith, Anne Gilsing & Parminder Raina

  2. McMaster Institute for Research on Aging, Hamilton, ON, Canada

    Ayesha A. Javed, Jinhui Ma, Laura N. Anderson, Alexandra J. Mayhew, Lauren E. Griffith, Anne Gilsing & Parminder Raina

  3. Labarge Centre for Mobility in Aging, Hamilton, ON, Canada

    Alexandra J. Mayhew, Lauren E. Griffith & Parminder Raina

  4. Department of Mathematics and Statistics, Oakland University, Rochester, MI, USA

    Hon Yiu So

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Contributions

AJ: conceptualized the research question, conducted analysis, and drafted the manuscript. JM: statistical analysis and writing. LA: analysis, writing, and interpretation. AM: contributed to analysis and writing. HS: statistical analysis and writing. LG: interpretation and writing. AG: design and writing. PR: design, data collection, analysis, interpretation, and writing.

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Correspondence to Parminder Raina.

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Javed, A.A., Ma, J., Anderson, L.N. et al. Age-appropriate BMI cut-points for cardiometabolic health risk: a cross-sectional analysis of the Canadian Longitudinal Study on Aging. Int J Obes 46, 1027–1035 (2022). https://doi.org/10.1038/s41366-022-01069-4

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